Abstract
In this study, 3D light detection and ranging (LiDAR) and an inertial measurement unit (IMU) were integrated on a ground vehicle for mapping tree fruit crops. A custom interface was developed in robot operating system for synchronous communication with hardware modules and for continuous field data collection. Point cloud data processing methods were developed for reconstruction and estimation of canopy parameters including height, voxel grid and convex hull methods-based volume and gap fractions in the canopy. The system was tested in apple tree and grapevine canopies. Overall, the mobile 3D LiDAR mapping system provided realistic representation of the canopy compared to the manual methods. For apple trees, the manual canopy volume measurements were strongly correlated to volume derived from the mobile 3D LiDAR mapping system (Convex hull: r = 0.81, Voxel grid: r = 0.51). The voxel grid method adequately considered gaps in the canopy during volume estimation and performed better than the convex hull method. The system was also evaluated for estimating canopy growth in grapevines with different rates of subsurface irrigation treatments. Deficit irrigation treatments did not show any significant effect on the canopy growth due to a high moisture content in the soil, resulting from high winter snowpack prior to that particular season. Nonetheless, the 3D LiDAR mapping system was able to aid in visualization of the temporal changes in canopy growth during the growing season. Change in vine canopy volume for the treatments followed a similar trend to the area ratio estimated from normalized differential vegetation index images derived from small unmanned aerial system based multispectral imagery. Overall, the 3D LiDAR based canopy mapping system and pertinent data mining algorithms can be the useful tool to the growers in rapid assessment of perennial fruit crop canopies for real-time management decision making.
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